Advertisement

REPRODUCE-ME: Ontology-Based Data Access for Reproducibility of Microscopy Experiments

  • Sheeba Samuel
  • Birgitta König-Ries
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10577)

Abstract

It has always been the aim of every scientist to make their work reproducible so that the scientific community can verify and trust the experiment results. With more complex in vivo and in vitro studies, achieving reproducibility has become more challenging over the last decades. In this work, we focus on integrative data management for reproducibility aspects related to execution environment conservation taking into account the use case of microscopy experiments. We use Semantic Web technologies to describe the experiment and its execution environment. We have developed an ontology, REPRODUCE-ME (Reproduce Microscopy Experiments) by extending the existing vocabulary PROV-O. Scientists can use this ontology to make semantic queries related to reproducibility of experiments on the microscopic data. To ensure efficient execution of these queries, we rely on ontology-based data access to source data stored in a relational DBMS.

Keywords

Reproducibility Experiments Ontology OBDA Microscopy 

Notes

Acknowledgements

This research is supported by the “Deutsche Forschungsgemeinschaft” (DFG) in Project Z2 of the CRC/TRR 166 “High-end light microscopy elucidates membrane receptor function - ReceptorLight”. Birgitta König-Ries was on sabbatical at DFG FZT 118 iDiv while working on this paper. We thank Christoph Biskup and Kathrin Groeneveld from University Hospital Jena, Germany, for providing the requirements to develop the proposed approach and validating the system and our colleagues Martin Bücker, Frank Taubert und Daniel Walther for their feedback.

References

  1. 1.
    Allan, C., Burel, J.M., Moore, J., Blackburn, C., Linkert, M., Loynton, S., MacDonald, D., Moore, W.J., Neves, C., Patterson, A., et al.: OMERO: flexible, model-driven data management for experimental biology. Nature Methods 9(3), 245–253 (2012)CrossRefGoogle Scholar
  2. 2.
    Compton, M., Corsar, D., Taylor, K.: Sensor data provenance: SSNO and PROV-O together at last. In: Terra Cognita and Semantic Sensor, Networks, pp. 67–82 (2014)Google Scholar
  3. 3.
    Jupp, S., Malone, J., Burdett, T., Heriche, J.K., Williams, E., Ellenberg, J., Parkinson, H., Rustici, G.: The cellular microscopy phenotype ontology. J. Biomed. Semant. 7(1), 28 (2016). http://dx.doi.org/10.1186/s13326-016-0074-0
  4. 4.
    Lebo, T., Sahoo, S., McGuinness, D., Belhajjame, K., Cheney, J., Corsar, D., Garijo, D., Soiland-Reyes, S., Zednik, S., Zhao, J.: PROV-O: the PROV ontology. W3C Recommendation 30 (2013)Google Scholar
  5. 5.
    Samuel, S.: Integrative data management for reproducibility of microscopy experiments. In: Blomqvist, E., Maynard, D., Gangemi, A., Hoekstra, R., Hitzler, P., Hartig, O. (eds.) ESWC 2017. LNCS, vol. 10250, pp. 246–255. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58451-5_19CrossRefGoogle Scholar
  6. 6.
    Samuel, S., Taubert, F., Walther, D., König-Ries, B., Bücker, H.M.: Towards reproducibility of microscopy experiments. D-Lib Magaz. 23(1/2) (2017)Google Scholar
  7. 7.
    Soldatova, L.N., King, R.D.: An ontology of scientific experiments. J. R. Soc. Interface 3(11), 795–803 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Heinz-Nixdorf Chair for Distributed Information SystemsFriedrich-Schiller UniversityJenaGermany

Personalised recommendations